Compositions for inferring bovine traits

Abstract

Methods, compositions, and systems are provided for managing bovine subjects in order to maximize their individual potential performance and edible meat value, and to maximize profits obtained in marketing the bovine subjects. The methods and systems draw an inference of a trait of a bovine subject by determining the nucleotide occurrence of at least one bovine SNP that is identified herein as being associated with the trait. The inference is used in methods of the present invention to establish the economic value of a bovine subject, to improve profits related to selling beef from a bovine subject; to manage bovine subjects, to sort bovine subjects; to improve the genetics of a bovine population by selecting and breeding of bovine subjects, to clone a bovine subject with a specific trait, to track meat or another commercial product of a bovine subject; and to diagnose a health condition of a bovine subject. Methods are also disclosed for identifying additional SNPs associated with a trait, by using the associated SNPs identified herein.

Description

CROSS REFERENCE TO RELATED APPLICATION [0001] This application claims the benefit of priority under 35 U.S.C. § 119(e) of U.S. Ser. No. 60 / 437,482, filed Dec. 31, 2002, the entire content of which is incorporated herein by reference.BACKGROUND OF THE INVENTION FIELD OF THE INVENTION [0002] The invention relates generally to gene association analyses and more specifically to polymorphisms and associated traits of bovine species. BACKGROUND INFORMATION [0003] Under the current standards established by the United States Department of Agriculture (USDA), beef from bulls, steers, and heifers is classified into eight different quality grades. Beginning with the highest and continuing to the lowest, the eight quality grades are prime, choice, select, standard, commercial, utility, cutter and canner. The characteristics which are used to classify beef include age, color, texture, firmness, and marbling, a term which is used to describe the relative amount of intramuscular fat of the beef. W...

AI Technical Summary

Benefits of technology

[0023] The present invention provides methods, systems, and compositions that allow the identification and selection of cattle with superior genetic potential for desirable characteristics. Accordingly, the present invention provides methods, compositions, and systems for managing, selecting and mating, breeding, and cloning cattle. These methods for identification and monitoring of key characteristics of individual animals and management of individual animals maximize their individual pote

Problems solved by technology

However, the visual appraisal of a beef carcass cannot occur until the animal is harvested.

Ultrasound can be used to give an indication of marbling prior to slaughter, but accuracy is low if ultrasound is done at a time significantly prior to harvest.

Currently there are no cost effective methods for identifying live cattle that give accurate prediction of the genetic potential to produce beef that is well-marbled.

Currently there are no procedures for identifying live animals whose beef, if cooked properly, would be tender.

Neither of these procedures can be used to any practical effect in a fabrication setting as the need to age product prior to testing would lead to maintenance of inventory of fabricated product that would be cost prohibitive.

It has been difficult for the livestock industry to combine genetics for red meat yield and marbling and/or tenderness.

In fact, conventional measurement techniques indicate that marbling and red meat yield tend to be antagonistic.

Currently, cattle producers do not have tools to identify animals with superior genetic potential for rapid growth prior to purchase.

In addition, there are no methods currently available to identify animals which combine capability for superior growth rate with desirable carcass characteristics.

While many methods of measurement and selection of cattle in feedlots have been tried, both visual and automated, such as ultrasound, none have been successful in accomplishing the desired end result.

Yet to date, it has been unable to devise a system or method to accomplish on a large scale what is needed to manage the current diversity of cattle (i.e. least about 100 different breeds and co-mingled breeds) to improve the beef product quality and uniformity fast enough to remain competitive in the race for the consumer dollar spent on meat.

While the classical breeding approach has produced steady genetic improvement in livestock species it is limited by the fact that accurate prediction of an individual's genetic potential can only be achieved when the animal reaches adulthood (fertility and production traits) or is harvested (meat quality traits).

This is particularly problematic for meat animals since harvested animals obviously cannot enter the breeding pool.

Furthermore, it is difficult to utilize the classical breeding approach for traits that are difficult (disease resistance) or costly (meat tenderness) to measure.

Limited success has been achieved for each of these methods in identifying genes that contribute to genetic variation for defined traits.

However, each method also has limitations, as the primary objectives of the molecular breeding approach described above have not been achieved.

However, this approach does have limitations and is analogous to finding a needle-in-the-haystack.

With over 30,000 genes characterized in humans and mouse as a result of the whole genome sequence the first difficulty is identifying a gene that will actually contribute to genetic variation for a specific trait.

Secondly, a large enough set of individuals must be sequenced to find the sequence variant that is responsible for or at least highly associated with the effect.

While it is feasible to meet all of these conditions to discover significant associations the cost of this approach is high because it is a random method that cannot be targeted to genes that have the largest effect.

Differential gene expression has been effective in identifying genes that are turned on or off by extreme differences in environment or by disease, but has been less successful in identifying genes that contribute to phenotypic variation